Nestor Michelena

Target Cascading in Optimal System Design

Target cascading is a key challenge in the early product development stages of large complex artifacts: how to propagate the desirable top level design specifications (or targets) to appropriate specifications for the various subsystems and components in a consistent and efficient manner. Consistency means that all parts of the designed system should work well together, while efficiency means that the process itself should avoid iterations at later stages, which are costly in time and resources. In the present article target cascading is formalized by a process modeled as a multilevel optimal design problem. Design targets are cascaded down to lower levels using partitioning of the original problem into a hierarchical set of subproblems. For each design problem at a given level, an optimisation problem is formulated to minimize deviations from the propagated targets and thus achieve intersystem compatibility. A coordination strategy links all subproblem decisions so that the overall system performance targets are met. The process is illustrated with an explicit analytical problem and a simple automotive chassis design model that demonstrates how the process can be applied in practice.

Convergence properties of analytical target cascading

Analytical target cascading (ATC) is a relatively new methodology for the design of engineering systems. ATC deals with the issue of propagating desirable top level product design specifications (or targets) to appropriate targets at lower levels in a consistent and ecient manner. Most existing problem formulations for multilevel design often exhibit convergence diculties. In this article, it is proved that under convexity assumptions the ATC process converges to the optimal solution of the original design target problem.

Target cascading in vehicle redesign: a class VI truck study

The analytical target cascading process is applied to the redesign of a U.S. class VI truck. Necessary simulation and analysis models for predicting vehicle dynamics, powertrain, and suspension behaviour are developed. Vehicle design targets that include improved fuel economy, ride quality, driveability, and performance metrics are translated into system design specifications, and a consistent final design is obtained. Trade-offs between conflicting targets are identified. The study illustrates how the analytical target cascading process can reduce vehicle design cycle time while ensuring physical prototype matching, and how costly design iterations late in the development process can be avoided.

A Hypergraph Framework for Optimal Model-Based Decomposition ofDesign Problems

Decomposition of large engineering system models is desirable sinceincreased model size reduces reliability and speed of numericalsolution algorithms. The article presents a methodology for optimalmodel-based decomposition (OMBD) of design problems, whether or notinitially cast as optimization problems. The overall model isrepresented by a hypergraph and is optimally partitioned into weaklyconnected subgraphs that satisfy decomposition constraints. Spectralgraph-partitioning methods together with iterative improvementtechniques are proposed for hypergraph partitioning. A known spectralK-partitioning formulation, which accounts for partition sizes andedge weights, is extended to graphs with also vertex weights. TheOMBD formulation is robust enough to account for computationaldemands and resources and strength of interdependencies between thecomputational modules contained in the model.

Optimization Approach to Hybrid Electric Propulsion System Design

Abstract Environmentally friendly ground vehicles with range and performance capabilities surpassing those of conventional vehicles require a careful balance among competing goals for fuel efficiency, performance, and emissions. The research objective here is to integrate hybrid electric vehicle simulations with high-fidelity engine modules, to increase the accuracy of predictions, and to allow design studies in the concept evaluation stage. This paper describes a methodology for integrating vehicle and engine simulations. The feed-forward model of the engine is modified to allow its linking with the vehicle model, and an engine component scaling routine is added to facilitate system sizing studies. A design optimization framework is then used to find the best overall engine size, battery pack, and motor combination for minimum fuel consumption within proposed US government performance criteria.

Optimal design of automotive hybrid powertrain systems

Alternative powertrains for automotive applications aim at improving emissions and fuel economy. Lack of experience with these relatively new technologies makes them ideal applications for computer-based modeling and simulation studies. There is a variety of configurations, control strategies, and design variable choices that can be made. If mathematical models exist, rigorous optimization techniques can be used to explore the design space. This paper provides an overview of a design environment for alternative powertrains that has these characteristics: modularity, allowing a system to be built by combining components; flexibility allowing different levels of fidelity and different existing codes to be used; and, rigor, since it is based an mathematical methods of decision making. A simple application to a hybrid diesel-electric powertrain is included.

Hierarchical Overlapping Coordination for Large-Scale Optimization by Decomposition

Decomposition of large engineering design problems into smaller design subproblems enhances robustness and speed of numerical solution algorithms. Design subproblems can be solved in parallel, using the optimization technique most suitable for the underlying subproblem. This also reflects the typical multidisciplinary nature of system design problems and allows better interpretation of results. Hierarchical overlapping coordination (HOC) simultaneously uses two or more problem decompositions, each of them associated with different partitions of the design variables and constraints. Coordination is achieved by the exchange of information between decompositions. We present the HOC algorithm and a sufficient condition for global convergence of the algorithm to the solution of a convex optimization problem. The convergence condition involves the rank of a matrix derived from the Jacobian of the constraints. Computational results obtained by applying the HOC algorithm to problems of various sizes are also presented.

Simulation-based optimal design of heavy trucks by model-based decomposition: an extensive analytical target cascading case study

We present the findings of an extensive case study for the decomposed, simulation-based, optimal design of an advanced technology heavy truck by means of analytical target cascading. The use of a series hybrid-electric propulsion system, in-hub motors, and variable height suspensions is considered with the intention of improving both commercial and military design attributes according to a dual-use philosophy. Emphasis is given to fuel economy, ride, and mobility characteristics. The latter are predicted by appropriately developed analytical and simulation models. This article builds on previous work and focuses on recent efforts to refine the applied methodologies and draw final conclusions.

A Network Reliability Approach to Optimal Decomposition of Design Problems

Methods for solving partitioned mathematical programming problems require that an appropriate structure suitable for decomposition be identified. This first step consists of identifying linking variables that effect independent subproblems coordinated by a master problem. This article presents a network reliability-based solution of the optimal decomposition problem that avoids subjective criteria to identify linking variables and partitions. The relationships among design variables are modeled as the processing units of a network. The design variables themselves are modeled as the communication links between these units. The optimal decomposition is attained by minimizing the network reliability while maximizing the number of operating links.

Optimal Model-Based Decomposition of Powertrain System Design

Optimal design of large engineering systems modeled as nonlinear programming problems remains a challenge because increased size reduces reliability and speed of numerical optimization algorithms. Decomposition of the original model into smaller coordinated submodels is desirable or even necessary. The article presents a methodology for optimal model-based decomposition of design problems, whether or not initially cast as optimization models. The overall model is represented by a hypergraph that is optimally partitioned into weakly-connected subgraphs satisfying partitioning constraints. The formulation is robust enough to account for computational demands and resources, and the strength of interdependencies between the design relations contained in the model. This decomposition methodology is applied to a vehicle powertrain system design model consisting of engine, torque converter, transmission, and wheel-tire assemblies, with 87 design relations and 119 design and state/behavior variables.